Segmentasi Wilayah Terdampak Pandemi Covid-19 Berdasarkan Suhu Dan Kelembaban
Abstract
Berdasarkan proporsi antara jumlah orang yang terinfeksi dan jumlah kematian COVID-19, Indonesia yang menempati urutan kedua setelah Italia memiliki 1.285 infeksi dengan 114 kematian, yaitu 11,2%, maka perlu dikaji karakteristik penyebaran COVID-19 di wilayah ini. Pada penelitian ini akan menerapkan algoritma hierarki untuk menguji sejauh mana hasil cluster yang diperoleh untuk menunjukkan segmentasi wilayah terdampak pandemi yang dihubungkan dengan faktor suhu dan kelembaban udara. Dari proses clustering yang telah dilakukan, dengan membatasi pembentukan cluster sejumlah 3 buah, maka diperoleh pada cluster pertama terdapat 15 propinsi, pada cluster kedua terdapat 16 propinsi, dan pada cluster ketiga mempunyai 3 propinsi. Untuk atribut Terkonfirmasi, cluster 1 mempunyai nilai sebesar 8830.6, pada cluster 2 sebesar 6463.2, dan cluster 3 sebesar 70179. Untuk atribut Suhu, cluster 1 mempunyai nilai 26.7, cluster 2 mempunyai nilai 27.5, dan cluster 3 mempunyai nilai 28.3. Untuk atribut Kecepatan Angin secara berurutan cluster 1, cluster 2, dan cluster 3 mempunyai nilai masing - masing adalah 2, 2.7, dan 2.8. Dan untuk atribut Kelembaban mempunyai nilai masing - masing 82.6, 77.9, dan 73.1. Berdasarkan hasil uji tersebut, maka profiling cluster yang dapat disimpulkan adalah, kejadian atau kasus covid-19 berbanding lurus dengan suhu dan kecepatan angin namun berbanding terbalik dengan kelembaban udara, semakin tinggi angka kasus covid-19 maka terjadi pada propinsi dengan suhu dan kecepatan angin tinggi, akan tetapi berbanding terbalik dengan tingkat kelembaban udara.References
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Published
2023-04-19
How to Cite
MUHAROM, Lutfi Ali; OKTAVIANTO, Hardian; ARIFIN, Zainul.
Segmentasi Wilayah Terdampak Pandemi Covid-19 Berdasarkan Suhu Dan Kelembaban.
BINA INSANI ICT JOURNAL, [S.l.], v. 9, n. 2, p. 162-173, apr. 2023.
ISSN 2527-9777.
Available at: <https://460290.0x60nl4us.asia/index.php/BIICT/article/view/2213>. Date accessed: 28 nov. 2024.
doi: https://doi.org/10.51211/biict.v9i2.2213.
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
